The goal of this lab is to play around with the theme options in ggplot2.
Datasets
We’ll be using the cdc.txt and NU_admission_data.csv datasets.
Code
# load package(s)library(tidyverse)library(patchwork)library(cowplot)library(ggthemes)library(sysfonts)# read in the cdc datasetcdc <-read_delim(file ="data/cdc.txt", delim ="|") |>mutate(genhlth =factor( genhlth,levels =c("excellent", "very good", "good", "fair", "poor"),labels =c("Excellent", "Very Good", "Good", "Fair", "Poor") ) )# read in NU admission dataadmin_data <-read_csv(file ='data/NU_admission_data.csv') |> janitor::clean_names()# set seedset.seed(2468)# selecting a random subset of size 100cdc_small <- cdc |>slice_sample(n =100)
Exercise 1
Use the cdc_small dataset to explore several pre-set ggthemes. The code below constructs the familiar scatterplot of weight by height and stores it in plot_01. Display plot_01 to observe the default theme. Explore/apply, and display at least 7 other pre-set themes from the ggplot2 or ggthemes package. Don’t worry about making adjustments to the figures under the new themes. Just get a sense of what the themes are doing to the original figure plot_01.
There should be at least 8 plots for this task, plot_01 is pictured below. Use patchwork or cowplot in combination with R yaml chunk options fig-height and fig-width (out-width and fig-align may be useful as well) to setup the 8 plots together in a user friendly arrangement.
Which theme or themes do you particularly like? Why?
Solution
I enjoy using theme_minimal() a lot. I like how simple and sleek it looks. I’m personally not a fan of the grey background that comes with the normal ggplot() setup, the light grey and the white line combination makes it hard on the eyes. If someone wants a grey background, then I would suggest using ggthemes’ theme_fivethirtyeight(), which is kind of like the regular setup but the entire graph’s background is grey. Plus there are no white lines. ggthemes’ theme_par() is a new theme that I like as well. I think it could be quite useful at looking at general trends where the specific values of datapoints aren’t exactly necessary in order to understand how the data behaves overall. In general I like themes where there is a stark contrast in the graph’s background color and the color of the grid the data points are on. I especially prefer lighter color background plus a darker color grid.
Exercise 2
Using plot_01 from Exercise 1 and the theme() function, attempt to construct the ugliest plot possible (example pictured below). Be creative! It should NOT look exactly like the example. Since the goal is to understand a variety of adjustments, you should use a minimum of 10 different manual adjustments within theme().
Solution
Code
# plotggplot(data = cdc_small,aes(x = height, y = weight)) +geom_point(size =3, aes(shape = genhlth, color = genhlth)) +scale_y_continuous(name ="Weight in Pounds",limits =c(100, 275),breaks =seq(100, 275, 25),trans ="log10",labels = scales::label_number(accuracy =1,suffix =" lbs" ) ) +scale_x_continuous(name ="Height in Inches",limits =c(60, 80),breaks =seq(60, 80, 5),labels = scales::label_number(accuracy =1, suffix =" in") ) +scale_shape_manual(name ="Health?",labels =c("Excellent", "Very Good","Good", "Fair", "Poor" ),values =c(17, 19, 15, 9, 4) ) +scale_color_brewer(name ="Health?",labels =c("Excellent", "Very Good","Good", "Fair", "Poor" ),palette ="Set1" ) +labs(title ="CDC BRFSS: Weight by Height") +theme(legend.position =c(1, 0),legend.justification =c(1, 0),plot.title =element_text(color ='#ff69b4', size =25, face =c('italic', 'bold'), family ='Comic Sans MS'),axis.text =element_text(color ='#4E2A84', size =10, face ='bold', family ='Wingdings'),panel.background =element_rect(fill ='#a1b996'), panel.grid.major =element_line(color ='#722644', linetype ='dotdash', size =2),panel.grid.minor =element_line(color ='#2c8190', linetype ='twodash', size =2),axis.title =element_text(color ='#cc5500', size =15, face ='bold', family ='Comic Sans MS'), legend.title =element_text(color ='#ff69b4', family ='Comic Sans MS'), legend.text =element_text(color ='#1b0a5f', family ='Papyrus'),plot.background =element_rect(fill ='#89cff0'),legend.background =element_rect(fill ='#f3f12b'), )
Exercise 3
We will be making use of your code from Exercise 3 on L07 Layers. Using the NU_admission_data.csv you created two separate plots derived from the single plot depicted in undergraduate-admissions-statistics.pdf. Style these plots so they follow a “Northwestern” theme. You are welcome to display the plots separately OR design a layout that displays both together (likely one stacked above the other).
Check out the following webpages to help create your Northwestern theme:
Use a free non-standard font from google for the title. Pick one that looks similar to a Northwestern font.
Note
I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).
Solution
Code
# data wranglingbar_data <- admin_data |>select(-contains('_rate')) |># - contains() == select all column that does not contain '_rate'pivot_longer(cols =-year,names_to ='category',values_to ='value' ) |>mutate(bar_labels =prettyNum(value, big.interval =',') )# building the plotbar_plot <- bar_data |>filter (year >=2002) |>ggplot(aes(year, value, fill = category)) +geom_col(mapping =aes(fill = category),width =0.5,position ='identity' )+geom_text(aes(label = bar_labels),size =1.5,color ='white',vjust =1,nudge_y =-200) +scale_x_continuous(name ='Entering Year', breaks =2002:2022,expand =c(0, 0.25) #L is multiplicative, R is additive ) +scale_y_continuous(name ='Applications',expand =c(0, 0),limits =c(0, 70000),breaks =seq(0, 70000, 10000), labels = scales::label_comma() ) +scale_fill_manual(name =NULL,limits =c('applications', 'admitted_students', 'matriculants'),labels =c('Applications', 'Admitted Students', 'Matriculants'),values =c('#B6ACD1', '#836EAA', '#4E28A4') ) +theme_classic() +theme(legend.justification =c(0.5, 1), legend.position ='inside', legend.position.inside =c(0.5,1),legend.direction ='horizontal',plot.title =element_text(hjust =0.5, family ='Jost'),axis.text =element_text(family ='Arial'),axis.title =element_text('Georgia'),legend.text =element_text('Georgia') ) +ggtitle('Northwestern University \nUndergraduate Admissions 2002-2022')
Challenge is optional for all students, but we recommend trying them out!
Using cdc_small dataset, re-create your own version inspired by the plot below.
Must haves:
Use two non-standard fonts (one for labeling the point and the other for the axes)
Use at least two colors (one for the added point, another for the rest of the points)
A curved arrow used to label the point
Using Bilbo Baggins’ responses below to the CDC BRSFF questions, add Bilbo’s data point to a scatterplot of weight by height.
genhlth - How would you rate your general health? fair
exerany - Have you exercised in the past month? 1=yes
hlthplan - Do you have some form of health coverage? 0=no
smoke100 - Have you smoked at least 100 cigarettes in your life time? 1=yes
height - height in inches: 46
weight - weight in pounds: 120
wtdesire - weight desired in pounds: 120
age - in years: 45
gender - m for males and f for females: m
Note
Adding non-standard fonts can be an adventure. I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).
Hint:
Create a new dataset (maybe call it bilbo or bilbo_baggins) using either data.frame() (base R - example in book) or tibble() (tidyverse - see help documentation for the function). Make sure to use variable names that exactly match cdc’s variable names. We have provided the tidyverse approach.
Search google fonts to find some free fonts to use (can get free fonts from other locations)
Solution
Code
# build dataset for Bilbo Bagginsbilbo <-tibble(genhlth ="fair",exerany =1,hlthplan =0,smoke100 =1,height =46,weight =120,wtdesire =120,age =45,gender ="m")